FutureSim: Replaying World Events to Evaluate Adaptive Agents
This benchmark provides a realistic, dynamic environment for measuring open-ended adaptation in AI agents, addressing the need for evaluation in real-world temporal settings.
FutureSim replays real-world news events chronologically to evaluate AI agents' ability to forecast world events beyond their knowledge cutoff. Over a three-month period, the best agent achieved only 25% accuracy, and many agents performed worse than making no prediction.
AI agents are being increasingly deployed in dynamic, open-ended environments that require adapting to new information as it arrives. To efficiently measure this capability for realistic use-cases, we propose building grounded simulations that replay real-world events in the order they occurred. We build FutureSim, where agents forecast world events beyond their knowledge cutoff while interacting with a chronological replay of the world: real news articles arriving and questions resolving over the simulated period. We evaluate frontier agents in their native harness, testing their ability to predict world events over a three-month period from January to March 2026. FutureSim reveals a clear separation in their capabilities, with the best agent's accuracy being 25%, and many having worse Brier skill score than making no prediction at all. Through careful ablations, we show how FutureSim offers a realistic setting to study emerging research directions like long-horizon test-time adaptation, search, memory, and reasoning about uncertainty. Overall, we hope our benchmark design paves the way to measure AI progress on open-ended adaptation spanning long time-horizons in the real world.